Abstract
Face detection shows a challenging problem in the field of image analysis and computer vision and therefore it has received a great deal of attention over the last few years because of its many applications in various areas. In this paper we propose a new method for face detection using an Extended version of Bag of Visual Words (EBoVW). Two extensions of the original bag of visual words are made in this paper, fist, using Fuzzy C-means instead of K-means clustering and second is, building histogram of words using multiple dictionaries for each image. The performances of the original BoVW model with K-means and the proposed EBoVW are evaluated in terms of Area Under the Curve (AUC) and Equal Error Rate (EER) on MIT CBCL Face dataset which is a very large face dataset. The experimental results show the proposed model achieves very promising results.
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Montazer, G.A., Soltanshahi, M.A., Giveki, D. (2015). Extended Bag of Visual Words for Face Detection. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9094. Springer, Cham. https://doi.org/10.1007/978-3-319-19258-1_41
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DOI: https://doi.org/10.1007/978-3-319-19258-1_41
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